Set path Laura: ONLY USE FOR LAURA
# base_path <- "//home.kt.ktzh.ch/B117T23$/Desktop/Riskktaking/Data"
base_path <- "/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/Data_S3"
Library
library(tidyverse)
library(ggplot2)
library(specr)
library(specr)
library(readxl)
library(ggthemes)
library(cowplot)
library(dplyr)
library(knitr)
library(kableExtra)
library(Hmisc)
remotes::install_github("masurp/specr")
library(ggplot2)
library(cowplot)
Load all data
# Combined GPS WVS
risktaking <- read.csv(file.path(base_path, "Specification_curve.csv"), header=TRUE, as.is=TRUE)
# Umbenennen von 'isocode' zu 'COUNTRY' in risktaking
risktaking <- risktaking %>%
rename(COUNTRY = isocode)
risktaking <- risktaking %>%
select(-hardship_index, -worldmap, -source)
hardship_hs <- read.csv(file.path(base_path, "hardship_HS.csv"))
hardship_finance <- read.csv(file.path(base_path, "hardship_finance.csv"))
hardship_crime <- read.csv(file.path(base_path, "hardship_crime.csv"))
hardship_environment <- read.csv(file.path(base_path, "hardship_environment.csv"))
# Entfernen der nicht benötigten Spalten aus den Datensätzen vor dem Zusammenführen
hardship_hs <- hardship_hs %>%
select(-country, -avg_risktaking)
hardship_finance <- hardship_finance %>%
select(-country, -avg_risktaking)
hardship_crime <- hardship_crime %>%
select(-country, -avg_risktaking)
hardship_environment <- hardship_environment %>%
select(-country, -avg_risktaking)
# Zusammenführen der Datensätze
hardship_combined <- risktaking %>%
left_join(hardship_hs, by = "COUNTRY") %>%
left_join(hardship_finance, by = "COUNTRY") %>%
left_join(hardship_crime, by = "COUNTRY") %>%
left_join(hardship_environment, by = "COUNTRY")
# Überprüfung der Struktur des kombinierten Datensatzes
#str(hardship_combined)
head(hardship_combined)
## country COUNTRY gender age_scale age risktaking HS_alc_tax_wine
## 1 Turkey TUR 1 -0.9021528 26 60.19755 0.205358
## 2 Turkey TUR 1 0.4737750 50 53.86746 0.205358
## 3 Turkey TUR 1 -1.1888045 21 60.19755 0.205358
## 4 Turkey TUR 0 -1.0168135 24 62.71073 0.205358
## 5 Turkey TUR 0 -1.0168135 24 61.22168 0.205358
## 6 Turkey TUR 0 -1.3034651 19 69.63037 0.205358
## HS_alc_roaddeath HS_drg_treatment HS_nic_affordability HS_mh_policy
## 1 -0.962363 -0.08607804 0.03046158 0
## 2 -0.962363 -0.08607804 0.03046158 0
## 3 -0.962363 -0.08607804 0.03046158 0
## 4 -0.962363 -0.08607804 0.03046158 0
## 5 -0.962363 -0.08607804 0.03046158 0
## 6 -0.962363 -0.08607804 0.03046158 0
## HS_sex_gini HS_oth_obesity HS_oth_cleancooking HS_mh_mhhospit
## 1 0.08475973 -0.9201102 -0.4749541 -1.073742
## 2 0.08475973 -0.9201102 -0.4749541 -1.073742
## 3 0.08475973 -0.9201102 -0.4749541 -1.073742
## 4 0.08475973 -0.9201102 -0.4749541 -1.073742
## 5 0.08475973 -0.9201102 -0.4749541 -1.073742
## 6 0.08475973 -0.9201102 -0.4749541 -1.073742
## HS_sex_antiretroviral HS_original_lifeexpectancy HS_original_genderequality
## 1 -0.007855567 -0.1578527 -0.1967259
## 2 -0.007855567 -0.1578527 -0.1967259
## 3 -0.007855567 -0.1578527 -0.1967259
## 4 -0.007855567 -0.1578527 -0.1967259
## 5 -0.007855567 -0.1578527 -0.1967259
## 6 -0.007855567 -0.1578527 -0.1967259
## hardship_HS_index f_inv_acctownership_primaryedu f_oth_insfinsvcs_int
## 1 -0.1918856 -0.495424 -1.339809
## 2 -0.1918856 -0.495424 -1.339809
## 3 -0.1918856 -0.495424 -1.339809
## 4 -0.1918856 -0.495424 -1.339809
## 5 -0.1918856 -0.495424 -1.339809
## 6 -0.1918856 -0.495424 -1.339809
## f_hs_oopexp10 f_eco_gdpdefl_linked f_eco_cpi f_original_gdp f_original_gini
## 1 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 2 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 3 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 4 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 5 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 6 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## hardship_Finance_index c_bh_homicide c_bh_childmalt c_bh_violextchildprot
## 1 -0.009989512 0.2305293 0.2262505 -0.5852603
## 2 -0.009989512 0.2305293 0.2262505 -0.5852603
## 3 -0.009989512 0.2305293 0.2262505 -0.5852603
## 4 -0.009989512 0.2305293 0.2262505 -0.5852603
## 5 -0.009989512 0.2305293 0.2262505 -0.5852603
## 6 -0.009989512 0.2305293 0.2262505 -0.5852603
## c_bh_parviolenceprog c_bh_elderabuse c_theft_estcorruption c_oth_polstab
## 1 1.044673 -0.9626803 -0.005687925 0.7771958
## 2 1.044673 -0.9626803 -0.005687925 0.7771958
## 3 1.044673 -0.9626803 -0.005687925 0.7771958
## 4 1.044673 -0.9626803 -0.005687925 0.7771958
## 5 1.044673 -0.9626803 -0.005687925 0.7771958
## 6 1.044673 -0.9626803 -0.005687925 0.7771958
## hardship_Crime_index e_oth_drinkingwater e_exp_watersanithyg100k e_ses_gini
## 1 0.1285553 -0.4208726 0.1506879 0.09919834
## 2 0.1285553 -0.4208726 0.1506879 0.09919834
## 3 0.1285553 -0.4208726 0.1506879 0.09919834
## 4 0.1285553 -0.4208726 0.1506879 0.09919834
## 5 0.1285553 -0.4208726 0.1506879 0.09919834
## 6 0.1285553 -0.4208726 0.1506879 0.09919834
## e_ses_school e_exp_disaster e_exp_airdeath100k e_exp_watersanithyg
## 1 0.5982926 0.8649465 -0.1717911 0.0709064
## 2 0.5982926 0.8649465 -0.1717911 0.0709064
## 3 0.5982926 0.8649465 -0.1717911 0.0709064
## 4 0.5982926 0.8649465 -0.1717911 0.0709064
## 5 0.5982926 0.8649465 -0.1717911 0.0709064
## 6 0.5982926 0.8649465 -0.1717911 0.0709064
## hardship_environment_index
## 1 0.1627161
## 2 0.1627161
## 3 0.1627161
## 4 0.1627161
## 5 0.1627161
## 6 0.1627161
head(hardship_combined)
## country COUNTRY gender age_scale age risktaking HS_alc_tax_wine
## 1 Turkey TUR 1 -0.9021528 26 60.19755 0.205358
## 2 Turkey TUR 1 0.4737750 50 53.86746 0.205358
## 3 Turkey TUR 1 -1.1888045 21 60.19755 0.205358
## 4 Turkey TUR 0 -1.0168135 24 62.71073 0.205358
## 5 Turkey TUR 0 -1.0168135 24 61.22168 0.205358
## 6 Turkey TUR 0 -1.3034651 19 69.63037 0.205358
## HS_alc_roaddeath HS_drg_treatment HS_nic_affordability HS_mh_policy
## 1 -0.962363 -0.08607804 0.03046158 0
## 2 -0.962363 -0.08607804 0.03046158 0
## 3 -0.962363 -0.08607804 0.03046158 0
## 4 -0.962363 -0.08607804 0.03046158 0
## 5 -0.962363 -0.08607804 0.03046158 0
## 6 -0.962363 -0.08607804 0.03046158 0
## HS_sex_gini HS_oth_obesity HS_oth_cleancooking HS_mh_mhhospit
## 1 0.08475973 -0.9201102 -0.4749541 -1.073742
## 2 0.08475973 -0.9201102 -0.4749541 -1.073742
## 3 0.08475973 -0.9201102 -0.4749541 -1.073742
## 4 0.08475973 -0.9201102 -0.4749541 -1.073742
## 5 0.08475973 -0.9201102 -0.4749541 -1.073742
## 6 0.08475973 -0.9201102 -0.4749541 -1.073742
## HS_sex_antiretroviral HS_original_lifeexpectancy HS_original_genderequality
## 1 -0.007855567 -0.1578527 -0.1967259
## 2 -0.007855567 -0.1578527 -0.1967259
## 3 -0.007855567 -0.1578527 -0.1967259
## 4 -0.007855567 -0.1578527 -0.1967259
## 5 -0.007855567 -0.1578527 -0.1967259
## 6 -0.007855567 -0.1578527 -0.1967259
## hardship_HS_index f_inv_acctownership_primaryedu f_oth_insfinsvcs_int
## 1 -0.1918856 -0.495424 -1.339809
## 2 -0.1918856 -0.495424 -1.339809
## 3 -0.1918856 -0.495424 -1.339809
## 4 -0.1918856 -0.495424 -1.339809
## 5 -0.1918856 -0.495424 -1.339809
## 6 -0.1918856 -0.495424 -1.339809
## f_hs_oopexp10 f_eco_gdpdefl_linked f_eco_cpi f_original_gdp f_original_gini
## 1 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 2 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 3 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 4 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 5 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## 6 0.5745653 0.5080757 0.753765 -0.6480058 0.5769059
## hardship_Finance_index c_bh_homicide c_bh_childmalt c_bh_violextchildprot
## 1 -0.009989512 0.2305293 0.2262505 -0.5852603
## 2 -0.009989512 0.2305293 0.2262505 -0.5852603
## 3 -0.009989512 0.2305293 0.2262505 -0.5852603
## 4 -0.009989512 0.2305293 0.2262505 -0.5852603
## 5 -0.009989512 0.2305293 0.2262505 -0.5852603
## 6 -0.009989512 0.2305293 0.2262505 -0.5852603
## c_bh_parviolenceprog c_bh_elderabuse c_theft_estcorruption c_oth_polstab
## 1 1.044673 -0.9626803 -0.005687925 0.7771958
## 2 1.044673 -0.9626803 -0.005687925 0.7771958
## 3 1.044673 -0.9626803 -0.005687925 0.7771958
## 4 1.044673 -0.9626803 -0.005687925 0.7771958
## 5 1.044673 -0.9626803 -0.005687925 0.7771958
## 6 1.044673 -0.9626803 -0.005687925 0.7771958
## hardship_Crime_index e_oth_drinkingwater e_exp_watersanithyg100k e_ses_gini
## 1 0.1285553 -0.4208726 0.1506879 0.09919834
## 2 0.1285553 -0.4208726 0.1506879 0.09919834
## 3 0.1285553 -0.4208726 0.1506879 0.09919834
## 4 0.1285553 -0.4208726 0.1506879 0.09919834
## 5 0.1285553 -0.4208726 0.1506879 0.09919834
## 6 0.1285553 -0.4208726 0.1506879 0.09919834
## e_ses_school e_exp_disaster e_exp_airdeath100k e_exp_watersanithyg
## 1 0.5982926 0.8649465 -0.1717911 0.0709064
## 2 0.5982926 0.8649465 -0.1717911 0.0709064
## 3 0.5982926 0.8649465 -0.1717911 0.0709064
## 4 0.5982926 0.8649465 -0.1717911 0.0709064
## 5 0.5982926 0.8649465 -0.1717911 0.0709064
## 6 0.5982926 0.8649465 -0.1717911 0.0709064
## hardship_environment_index
## 1 0.1627161
## 2 0.1627161
## 3 0.1627161
## 4 0.1627161
## 5 0.1627161
## 6 0.1627161
if (!"age_category" %in% names(hardship_combined)) {
hardship_combined$age_category <- cut(hardship_combined$age,
breaks = c(15, 24, 34, 44, 54, 64, 74, 84, 99),
labels = c("Youth (15-24)",
"Young Adults (25-34)",
"Middle-aged Adults (35-44)",
"Mature Adults (45-54)",
"Pre-seniors (55-64)",
"Early Seniors (65-74)",
"Seniors (75-84)",
"Elderly (85-99)"),
right = TRUE, include.lowest = TRUE)
}
# Umwandlung der Alterskategorien in numerische Werte
hardship_combined$age_numeric <- as.integer(factor(hardship_combined$age_category))
# Überprüfung der neuen numerischen Alterskategorien
table(hardship_combined$age_numeric)
##
## 1 2 3 4 5 6 7 8
## 40700 51013 44658 37511 28953 17776 7335 1119
get_variable_categories <- function() {
list(
"Health/Safety" = c("HS_alc_tax_wine", "HS_alc_roaddeath", "HS_drg_treatment", "HS_nic_affordability",
"HS_mh_policy", "HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking", "HS_mh_mhhospit",
"HS_sex_antiretroviral", "HS_original_lifeexpectancy", "HS_original_genderequality", "hardship_HS_index"),
"Finance" = c("f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int", "f_hs_oopexp10", "f_eco_gdpdefl_linked",
"f_eco_cpi", "f_original_gdp", "f_original_gini", "hardship_Finance_index", "c_bh_homicide"),
"Crime" = c("c_bh_childmalt", "c_bh_violextchildprot", "c_bh_parviolenceprog", "c_bh_elderabuse",
"c_theft_estcorruption", "c_oth_polstab", "hardship_Crime_index"),
"Environment" = c("e_oth_drinkingwater", "e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school",
"e_exp_disaster", "e_exp_airdeath100k", "e_exp_watersanithyg", "hardship_environment_index")
)
}
Calculate correlation with risktaking
# Berechnung der Korrelation zwischen risktaking und den anderen Variablen
correlation_results <- cor(hardship_combined[, sapply(hardship_combined, is.numeric)], use = "complete.obs")
# Korrelationstabelle für risktaking extrahieren
risktaking_correlations <- correlation_results["risktaking", ]
# Konvertierung der Korrelationsergebnisse in ein formatiertes Datenframe
correlation_table <- data.frame(
Variable = names(risktaking_correlations),
Correlation = risktaking_correlations
)
# Entfernen der Korrelation von risktaking mit sich selbst
correlation_table <- correlation_table[correlation_table$Variable != "risktaking", ]
# Sortieren der Ergebnisse nach dem Betrag der Korrelation, absteigend
correlation_table <- correlation_table[order(-abs(correlation_table$Correlation)), ]
# Anzeigen der Tabelle
kable(correlation_table, caption = "Korrelationen zwischen risktaking und anderen Variablen") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
Korrelationen zwischen risktaking und anderen Variablen
|
|
Variable
|
Correlation
|
|
age_scale
|
age_scale
|
-0.2381482
|
|
age
|
age
|
-0.2381263
|
|
age_numeric
|
age_numeric
|
-0.2357508
|
|
hardship_Finance_index
|
hardship_Finance_index
|
0.1255989
|
|
HS_original_lifeexpectancy
|
HS_original_lifeexpectancy
|
0.1244475
|
|
hardship_environment_index
|
hardship_environment_index
|
0.1228625
|
|
gender
|
gender
|
-0.1201418
|
|
e_exp_watersanithyg100k
|
e_exp_watersanithyg100k
|
0.1159198
|
|
f_original_gini
|
f_original_gini
|
0.1127801
|
|
hardship_HS_index
|
hardship_HS_index
|
0.1122306
|
|
hardship_Crime_index
|
hardship_Crime_index
|
0.1043428
|
|
e_exp_airdeath100k
|
e_exp_airdeath100k
|
0.1020337
|
|
e_oth_drinkingwater
|
e_oth_drinkingwater
|
0.0974426
|
|
HS_oth_cleancooking
|
HS_oth_cleancooking
|
0.0967826
|
|
f_original_gdp
|
f_original_gdp
|
0.0955940
|
|
c_bh_violextchildprot
|
c_bh_violextchildprot
|
0.0936960
|
|
e_exp_watersanithyg
|
e_exp_watersanithyg
|
0.0926370
|
|
e_ses_gini
|
e_ses_gini
|
0.0913668
|
|
c_bh_childmalt
|
c_bh_childmalt
|
0.0876729
|
|
HS_alc_tax_wine
|
HS_alc_tax_wine
|
0.0867029
|
|
c_bh_homicide
|
c_bh_homicide
|
0.0814154
|
|
c_bh_elderabuse
|
c_bh_elderabuse
|
0.0796172
|
|
HS_drg_treatment
|
HS_drg_treatment
|
0.0779335
|
|
f_hs_oopexp10
|
f_hs_oopexp10
|
0.0754779
|
|
f_eco_cpi
|
f_eco_cpi
|
0.0751496
|
|
c_oth_polstab
|
c_oth_polstab
|
0.0744124
|
|
HS_sex_gini
|
HS_sex_gini
|
0.0728827
|
|
f_eco_gdpdefl_linked
|
f_eco_gdpdefl_linked
|
0.0726808
|
|
HS_alc_roaddeath
|
HS_alc_roaddeath
|
0.0699059
|
|
HS_nic_affordability
|
HS_nic_affordability
|
0.0641391
|
|
c_theft_estcorruption
|
c_theft_estcorruption
|
0.0559263
|
|
f_oth_insfinsvcs_int
|
f_oth_insfinsvcs_int
|
0.0538295
|
|
HS_original_genderequality
|
HS_original_genderequality
|
0.0496918
|
|
HS_sex_antiretroviral
|
HS_sex_antiretroviral
|
0.0495593
|
|
e_ses_school
|
e_ses_school
|
0.0468552
|
|
c_bh_parviolenceprog
|
c_bh_parviolenceprog
|
0.0421859
|
|
HS_oth_obesity
|
HS_oth_obesity
|
0.0363355
|
|
HS_mh_mhhospit
|
HS_mh_mhhospit
|
0.0209093
|
|
HS_mh_policy
|
HS_mh_policy
|
-0.0149841
|
|
f_inv_acctownership_primaryedu
|
f_inv_acctownership_primaryedu
|
0.0114821
|
|
e_exp_disaster
|
e_exp_disaster
|
0.0013854
|
Table with Correlation hardship factors and risktaking
# Laden notwendiger Bibliotheken
library(Hmisc)
library(kableExtra)
# Auswahl aller numerischen Variablen
numeric_vars <- hardship_combined %>%
select(where(is.numeric))
# Berechnen der Korrelationsmatrix und der p-Werte
cor_results <- rcorr(as.matrix(numeric_vars))
# Korrelationen und p-Werte spezifisch für 'risktaking' extrahieren
correlations <- cor_results$r[, "risktaking"] # Korrelationen zu 'risktaking'
p_values <- cor_results$P[, "risktaking"] # p-Werte zu 'risktaking'
# Datenrahmen für die Darstellung erstellen
cor_table <- data.frame(
Variable = rownames(cor_results$r), # Namen der Variablen
Correlation = round(correlations, 5), # Korrelationswerte, gerundet auf 5 Dezimalstellen
P_value = format(p_values, scientific = TRUE), # p-Werte in wissenschaftlicher Notation
Significant = ifelse(p_values < 0.05, "Yes", "No") # Signifikanzflag, basierend auf p-Wert
)
# Tabellendarstellung mit 'kable' und 'kableExtra'
cor_table %>%
kable("html", caption = "Correlations with Risktaking: Summary of Results") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
column_spec(2, bold = TRUE) %>%
column_spec(3, background = "lightyellow")
Correlations with Risktaking: Summary of Results
|
|
Variable
|
Correlation
|
P_value
|
Significant
|
|
gender
|
gender
|
-0.11984
|
0.000000e+00
|
Yes
|
|
age_scale
|
age_scale
|
-0.24197
|
0.000000e+00
|
Yes
|
|
age
|
age
|
-0.24184
|
0.000000e+00
|
Yes
|
|
risktaking
|
risktaking
|
1.00000
|
NA
|
NA
|
|
HS_alc_tax_wine
|
HS_alc_tax_wine
|
0.08408
|
0.000000e+00
|
Yes
|
|
HS_alc_roaddeath
|
HS_alc_roaddeath
|
0.07748
|
0.000000e+00
|
Yes
|
|
HS_drg_treatment
|
HS_drg_treatment
|
0.07606
|
0.000000e+00
|
Yes
|
|
HS_nic_affordability
|
HS_nic_affordability
|
0.07479
|
0.000000e+00
|
Yes
|
|
HS_mh_policy
|
HS_mh_policy
|
-0.00730
|
5.432256e-04
|
Yes
|
|
HS_sex_gini
|
HS_sex_gini
|
0.08042
|
0.000000e+00
|
Yes
|
|
HS_oth_obesity
|
HS_oth_obesity
|
0.01722
|
4.440892e-16
|
Yes
|
|
HS_oth_cleancooking
|
HS_oth_cleancooking
|
0.10040
|
0.000000e+00
|
Yes
|
|
HS_mh_mhhospit
|
HS_mh_mhhospit
|
0.03081
|
0.000000e+00
|
Yes
|
|
HS_sex_antiretroviral
|
HS_sex_antiretroviral
|
0.05036
|
0.000000e+00
|
Yes
|
|
HS_original_lifeexpectancy
|
HS_original_lifeexpectancy
|
0.13508
|
0.000000e+00
|
Yes
|
|
HS_original_genderequality
|
HS_original_genderequality
|
0.05193
|
0.000000e+00
|
Yes
|
|
hardship_HS_index
|
hardship_HS_index
|
0.11857
|
0.000000e+00
|
Yes
|
|
f_inv_acctownership_primaryedu
|
f_inv_acctownership_primaryedu
|
0.02165
|
0.000000e+00
|
Yes
|
|
f_oth_insfinsvcs_int
|
f_oth_insfinsvcs_int
|
0.05273
|
0.000000e+00
|
Yes
|
|
f_hs_oopexp10
|
f_hs_oopexp10
|
0.07968
|
0.000000e+00
|
Yes
|
|
f_eco_gdpdefl_linked
|
f_eco_gdpdefl_linked
|
0.07069
|
0.000000e+00
|
Yes
|
|
f_eco_cpi
|
f_eco_cpi
|
0.10108
|
0.000000e+00
|
Yes
|
|
f_original_gdp
|
f_original_gdp
|
0.10264
|
0.000000e+00
|
Yes
|
|
f_original_gini
|
f_original_gini
|
0.11561
|
0.000000e+00
|
Yes
|
|
hardship_Finance_index
|
hardship_Finance_index
|
0.13907
|
0.000000e+00
|
Yes
|
|
c_bh_homicide
|
c_bh_homicide
|
0.09742
|
0.000000e+00
|
Yes
|
|
c_bh_childmalt
|
c_bh_childmalt
|
0.09575
|
0.000000e+00
|
Yes
|
|
c_bh_violextchildprot
|
c_bh_violextchildprot
|
0.09740
|
0.000000e+00
|
Yes
|
|
c_bh_parviolenceprog
|
c_bh_parviolenceprog
|
0.04102
|
0.000000e+00
|
Yes
|
|
c_bh_elderabuse
|
c_bh_elderabuse
|
0.08784
|
0.000000e+00
|
Yes
|
|
c_theft_estcorruption
|
c_theft_estcorruption
|
0.06759
|
0.000000e+00
|
Yes
|
|
c_oth_polstab
|
c_oth_polstab
|
0.08484
|
0.000000e+00
|
Yes
|
|
hardship_Crime_index
|
hardship_Crime_index
|
0.11775
|
0.000000e+00
|
Yes
|
|
e_oth_drinkingwater
|
e_oth_drinkingwater
|
0.10202
|
0.000000e+00
|
Yes
|
|
e_exp_watersanithyg100k
|
e_exp_watersanithyg100k
|
0.12385
|
0.000000e+00
|
Yes
|
|
e_ses_gini
|
e_ses_gini
|
0.09615
|
0.000000e+00
|
Yes
|
|
e_ses_school
|
e_ses_school
|
0.04925
|
0.000000e+00
|
Yes
|
|
e_exp_disaster
|
e_exp_disaster
|
-0.00702
|
8.810206e-04
|
Yes
|
|
e_exp_airdeath100k
|
e_exp_airdeath100k
|
0.11396
|
0.000000e+00
|
Yes
|
|
e_exp_watersanithyg
|
e_exp_watersanithyg
|
0.09110
|
0.000000e+00
|
Yes
|
|
hardship_environment_index
|
hardship_environment_index
|
0.12948
|
0.000000e+00
|
Yes
|
|
age_numeric
|
age_numeric
|
-0.23943
|
0.000000e+00
|
Yes
|
Setup for specifications
library(specr)
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS_index", "f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index",
"c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_theft_estcorruption",
"c_oth_polstab", "hardship_Crime_index", "e_oth_drinkingwater",
"e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school", "e_exp_disaster",
"e_exp_airdeath100k", "e_exp_watersanithyg"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 36
##
## Specifications:
##
## Independent variable: HS_alc_tax_wine, HS_alc_roaddeath, HS_drg_treatment, HS_nic_affordability, HS_mh_policy, HS_sex_gini, HS_oth_obesity, HS_oth_cleancooking, HS_mh_mhhospit, HS_sex_antiretroviral, HS_original_lifeexpectancy, HS_original_genderequality, hardship_HS_index, f_inv_acctownership_primaryedu, f_oth_insfinsvcs_int, f_hs_oopexp10, f_eco_gdpdefl_linked, f_eco_cpi, f_original_gdp, f_original_gini, hardship_Finance_index, c_bh_homicide, c_bh_childmalt, c_bh_violextchildprot, c_bh_parviolenceprog, c_bh_elderabuse, c_theft_estcorruption, c_oth_polstab, hardship_Crime_index, e_oth_drinkingwater, e_exp_watersanithyg100k, e_ses_gini, e_ses_school, e_exp_disaster, e_exp_airdeath100k, e_exp_watersanithyg
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x126829040>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 HS_alc_tax_wine risktaking lm no covariates all risktaking ~ HS_a…
## 2 HS_alc_roaddeath risktaking lm no covariates all risktaking ~ HS_a…
## 3 HS_drg_treatment risktaking lm no covariates all risktaking ~ HS_d…
## 4 HS_nic_affordability risktaking lm no covariates all risktaking ~ HS_n…
## 5 HS_mh_policy risktaking lm no covariates all risktaking ~ HS_m…
## 6 HS_sex_gini risktaking lm no covariates all risktaking ~ HS_s…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 36 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.9 0.32 -0.17 2.46 0.67 1.1
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 5.92 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.89992 0.32447 -0.16974 2.46013 0.66647 1.09621
##
## Descriptive summary of sample sizes:
##
## median min max
## 224583 221536 225551
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.917 0.0229 40.0
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.853 0.0232 36.8
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.784 0.0217 36.1
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.806 0.0227 35.5
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.170 0.0491 -3.46
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.837 0.0219 38.2
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.900 0.324 -0.170 2.46 0.666 1.10 224583
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 36 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HS_alc_roaddeath 0.853 0.853 0.853 0.853 224583
## 2 HS_alc_tax_wine 0.917 0.917 0.917 0.917 224583
## 3 HS_drg_treatment 0.784 0.784 0.784 0.784 224583
## 4 HS_mh_mhhospit 0.327 0.327 0.327 0.327 224583
## 5 HS_mh_policy -0.170 -0.170 -0.170 -0.170 224583
## 6 HS_nic_affordability 0.806 0.806 0.806 0.806 224583
## 7 HS_original_genderequality 0.677 0.677 0.677 0.677 224583
## 8 HS_original_lifeexpectancy 1.40 1.40 1.40 1.40 224583
## 9 HS_oth_cleancooking 1.09 1.09 1.09 1.09 224583
## 10 HS_oth_obesity 0.169 0.169 0.169 0.169 224583
## # ℹ 26 more rows
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS_index", "f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index",
"c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_theft_estcorruption",
"c_oth_polstab", "hardship_Crime_index", "e_oth_drinkingwater",
"e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school", "e_exp_disaster",
"e_exp_airdeath100k", "e_exp_watersanithyg"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 3.628 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.99 0.38 -0.11 2.8 0.74 1.21
##
## Descriptive summary of sample sizes:
##
## median min max
## 119096 117485 119591
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.93 0.03 29.5
## 2 HS_alc_road… risk… lm no cova… all riskta… 1 0.03 31.2
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.83 0.03 28.6
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.94 0.03 30.0
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.11 0.07 -1.7
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.97 0.03 32.4
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS_index", "f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index",
"c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_theft_estcorruption",
"c_oth_polstab", "hardship_Crime_index", "e_oth_drinkingwater",
"e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school", "e_exp_disaster",
"e_exp_airdeath100k", "e_exp_watersanithyg"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 2.712 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.71 0.31 -0.26 2.04 0.52 0.93
##
## Descriptive summary of sample sizes:
##
## median min max
## 105487 104051 105960
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.84 0.03 25.4
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.69 0.03 20.9
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.7 0.03 21.8
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.61 0.03 18.9
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.26 0.07 -3.72
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.63 0.03 20.0
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for age-categories
run_specification_for_age <- function(data, age_id, age_label) {
# Daten für die spezifische Altersgruppe filtern
data_subset <- data %>%
filter(age_numeric == age_id)
# Setup für die Spezifikationen durchführen
specification <- setup(
data = data_subset,
y = "risktaking",
x = c("HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS_index", "f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index",
"c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_theft_estcorruption",
"c_oth_polstab", "hardship_Crime_index", "e_oth_drinkingwater",
"e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school", "e_exp_disaster",
"e_exp_airdeath100k", "e_exp_watersanithyg"),
model = "lm"
)
# Spezifikationsergebnisse berechnen
specification_results <- specr(specification)
# Statistische Auswertungen drucken mit Alterskategorie-Titel
cat("\nStatistische Ergebnisse für die Alterskategorie:", age_label, "\n")
print(summary(specification_results, digits = 5))
# Grafiken für die spezifische Altersgruppe erzeugen und anzeigen
plot_list <- list(
plot_a = plot(specification_results, type = "curve", ci = FALSE, ribbon = TRUE) +
geom_point(size = 4) + ggtitle(paste("Curve Plot -", age_label)),
plot_b = plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4) + ggtitle(paste("Choices Plot -", age_label)),
plot_c = plot(specification_results, type = "samplesizes") + ylim(0, 400) +
ggtitle(paste("Sample Sizes Plot -", age_label)),
plot_d = plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) + scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "") + ggtitle(paste("Boxplot -", age_label))
)
# Rückgabe der Ergebnisse und Plots
return(list(summary = summary(specification_results, digits = 5), plots = plot_list))
}
# Funktion für jede Altersgruppe aufrufen und sowohl statistische Zusammenfassungen als auch Plots ausgeben
for (i in 1:8) {
results <- run_specification_for_age(hardship_combined, i, paste("Age Group", i))
print(results$summary) # Drucke die Zusammenfassung der Ergebnisse
print(results$plots$plot_a)
print(results$plots$plot_b)
print(results$plots$plot_c)
print(results$plots$plot_d)
}
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 1
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.919 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## -0.09126 0.34031 -1.21618 0.68027 -0.32629 0.08934
##
## Descriptive summary of sample sizes:
##
## median min max
## 40131 39992 40248
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.380 0.0533 7.13
## 2 HS_alc_road… risk… lm no cova… all riskta… -0.0688 0.0474 -1.45
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.137 0.0492 2.79
## 4 HS_nic_affo… risk… lm no cova… all riskta… -0.322 0.0497 -6.47
## 5 HS_mh_policy risk… lm no cova… all riskta… -1.22 0.112 -10.9
## 6 HS_sex_gini risk… lm no cova… all riskta… -0.541 0.0512 -10.6
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.919 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## -0.09126 0.34031 -1.21618 0.68027 -0.32629 0.08934
##
## Descriptive summary of sample sizes:
##
## median min max
## 40131 39992 40248
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.380 0.0533 7.13
## 2 HS_alc_road… risk… lm no cova… all riskta… -0.0688 0.0474 -1.45
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.137 0.0492 2.79
## 4 HS_nic_affo… risk… lm no cova… all riskta… -0.322 0.0497 -6.47
## 5 HS_mh_policy risk… lm no cova… all riskta… -1.22 0.112 -10.9
## 6 HS_sex_gini risk… lm no cova… all riskta… -0.541 0.0512 -10.6
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 2
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.274 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.55414 0.33273 -0.62666 1.678 0.17235 0.74247
##
## Descriptive summary of sample sizes:
##
## median min max
## 50053 49763 50291
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.394 0.0479 8.22
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.596 0.0440 13.6
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.460 0.0452 10.2
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.422 0.0440 9.58
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.627 0.101 -6.20
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.0164 0.0457 0.358
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.274 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.55414 0.33273 -0.62666 1.678 0.17235 0.74247
##
## Descriptive summary of sample sizes:
##
## median min max
## 50053 49763 50291
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.394 0.0479 8.22
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.596 0.0440 13.6
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.460 0.0452 10.2
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.422 0.0440 9.58
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.627 0.101 -6.20
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.0164 0.0457 0.358
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 3
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.974 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.6992 0.31182 -0.26951 1.87 0.39867 0.82467
##
## Descriptive summary of sample sizes:
##
## median min max
## 43739 43230 43981
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.709 0.0509 13.9
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.782 0.0516 15.2
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.623 0.0483 12.9
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.616 0.0494 12.5
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.161 0.108 -1.49
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.447 0.0492 9.09
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.974 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.6992 0.31182 -0.26951 1.87 0.39867 0.82467
##
## Descriptive summary of sample sizes:
##
## median min max
## 43739 43230 43981
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.709 0.0509 13.9
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.782 0.0516 15.2
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.623 0.0483 12.9
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.616 0.0494 12.5
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.161 0.108 -1.49
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.447 0.0492 9.09
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 4
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.851 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.62911 0.28999 -0.263 1.74192 0.32607 0.74046
##
## Descriptive summary of sample sizes:
##
## median min max
## 36643 36047 36781
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.686 0.0562 12.2
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.667 0.0608 11.0
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.604 0.0527 11.5
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.611 0.0578 10.6
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.121 0.119 1.02
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.493 0.0544 9.07
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.851 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.62911 0.28999 -0.263 1.74192 0.32607 0.74046
##
## Descriptive summary of sample sizes:
##
## median min max
## 36643 36047 36781
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.686 0.0562 12.2
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.667 0.0608 11.0
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.604 0.0527 11.5
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.611 0.0578 10.6
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.121 0.119 1.02
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.493 0.0544 9.07
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 5
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.689 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.77887 0.34704 -0.06869 2.55015 0.56418 1.0168
##
## Descriptive summary of sample sizes:
##
## median min max
## 28345 27546 28417
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.782 0.0641 12.2
## 2 HS_alc_road… risk… lm no cova… all riskta… 1.04 0.0750 13.8
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.699 0.0597 11.7
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.854 0.0697 12.3
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.278 0.136 2.05
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.742 0.0632 11.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.689 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.77887 0.34704 -0.06869 2.55015 0.56418 1.0168
##
## Descriptive summary of sample sizes:
##
## median min max
## 28345 27546 28417
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.782 0.0641 12.2
## 2 HS_alc_road… risk… lm no cova… all riskta… 1.04 0.0750 13.8
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.699 0.0597 11.7
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.854 0.0697 12.3
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.278 0.136 2.05
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.742 0.0632 11.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 6
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.473 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.82553 0.51379 0.06936 2.82099 0.39879 1.02854
##
## Descriptive summary of sample sizes:
##
## median min max
## 17485 16909 17519
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.912 0.0777 11.7
## 2 HS_alc_road… risk… lm no cova… all riskta… 1.02 0.0982 10.4
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.849 0.0765 11.1
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.896 0.0932 9.62
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.292 0.171 1.70
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.980 0.0825 11.9
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.473 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.82553 0.51379 0.06936 2.82099 0.39879 1.02854
##
## Descriptive summary of sample sizes:
##
## median min max
## 17485 16909 17519
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.912 0.0777 11.7
## 2 HS_alc_road… risk… lm no cova… all riskta… 1.02 0.0982 10.4
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.849 0.0765 11.1
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.896 0.0932 9.62
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.292 0.171 1.70
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.980 0.0825 11.9
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 7
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.283 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.94319 0.5109 -0.18088 3.61082 0.60599 1.2869
##
## Descriptive summary of sample sizes:
##
## median min max
## 7198 6951 7204
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.957 0.120 7.96
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.901 0.162 5.55
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.934 0.117 7.96
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.952 0.155 6.13
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.00237 0.259 0.00915
## 6 HS_sex_gini risk… lm no cova… all riskta… 1.42 0.136 10.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.283 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.94319 0.5109 -0.18088 3.61082 0.60599 1.2869
##
## Descriptive summary of sample sizes:
##
## median min max
## 7198 6951 7204
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.957 0.120 7.96
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.901 0.162 5.55
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.934 0.117 7.96
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.952 0.155 6.13
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.00237 0.259 0.00915
## 6 HS_sex_gini risk… lm no cova… all riskta… 1.42 0.136 10.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 8
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.141 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.002 0.73912 0.16285 3.96158 0.54212 1.54371
##
## Descriptive summary of sample sizes:
##
## median min max
## 1109 1098 1110
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.549 0.304 1.81
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.586 0.438 1.34
## 3 HS_drg_trea… risk… lm no cova… all riskta… 1.21 0.332 3.65
## 4 HS_nic_affo… risk… lm no cova… all riskta… 1.40 0.394 3.56
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.879 0.677 1.30
## 6 HS_sex_gini risk… lm no cova… all riskta… 1.51 0.333 4.51
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.141 sec elapsed
## Number of specifications: 36
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.002 0.73912 0.16285 3.96158 0.54212 1.54371
##
## Descriptive summary of sample sizes:
##
## median min max
## 1109 1098 1110
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.549 0.304 1.81
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.586 0.438 1.34
## 3 HS_drg_trea… risk… lm no cova… all riskta… 1.21 0.332 3.65
## 4 HS_nic_affo… risk… lm no cova… all riskta… 1.40 0.394 3.56
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.879 0.677 1.30
## 6 HS_sex_gini risk… lm no cova… all riskta… 1.51 0.333 4.51
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
Hardship Health/Safety
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS_index"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 13
##
## Specifications:
##
## Independent variable: HS_alc_tax_wine, HS_alc_roaddeath, HS_drg_treatment, HS_nic_affordability, HS_mh_policy, HS_sex_gini, HS_oth_obesity, HS_oth_cleancooking, HS_mh_mhhospit, HS_sex_antiretroviral, HS_original_lifeexpectancy, HS_original_genderequality, hardship_HS_index
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x117eecb70>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 HS_alc_tax_wine risktaking lm no covariates all risktaking ~ HS_a…
## 2 HS_alc_roaddeath risktaking lm no covariates all risktaking ~ HS_a…
## 3 HS_drg_treatment risktaking lm no covariates all risktaking ~ HS_d…
## 4 HS_nic_affordability risktaking lm no covariates all risktaking ~ HS_n…
## 5 HS_mh_policy risktaking lm no covariates all risktaking ~ HS_m…
## 6 HS_sex_gini risktaking lm no covariates all risktaking ~ HS_s…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 13 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.81 0.28 -0.17 2.46 0.62 0.92
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 2.508 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.80569 0.27765 -0.16974 2.46013 0.61842 0.91657
##
## Descriptive summary of sample sizes:
##
## median min max
## 224583 224583 224583
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.917 0.0229 40.0
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.853 0.0232 36.8
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.784 0.0217 36.1
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.806 0.0227 35.5
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.170 0.0491 -3.46
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.837 0.0219 38.2
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 0.806 0.278 -0.170 2.46 0.618 0.917 224583
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 13 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 HS_alc_roaddeath 0.853 0.853 0.853 0.853 224583
## 2 HS_alc_tax_wine 0.917 0.917 0.917 0.917 224583
## 3 HS_drg_treatment 0.784 0.784 0.784 0.784 224583
## 4 HS_mh_mhhospit 0.327 0.327 0.327 0.327 224583
## 5 HS_mh_policy -0.170 -0.170 -0.170 -0.170 224583
## 6 HS_nic_affordability 0.806 0.806 0.806 0.806 224583
## 7 HS_original_genderequality 0.677 0.677 0.677 0.677 224583
## 8 HS_original_lifeexpectancy 1.40 1.40 1.40 1.40 224583
## 9 HS_oth_cleancooking 1.09 1.09 1.09 1.09 224583
## 10 HS_oth_obesity 0.169 0.169 0.169 0.169 224583
## 11 HS_sex_antiretroviral 0.618 0.618 0.618 0.618 224583
## 12 HS_sex_gini 0.837 0.837 0.837 0.837 224583
## 13 hardship_HS_index 2.46 2.46 2.46 2.46 224583
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2.5, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS_index"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.022 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.93 0.39 -0.11 2.8 0.66 1
##
## Descriptive summary of sample sizes:
##
## median min max
## 119096 119096 119096
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.93 0.03 29.5
## 2 HS_alc_road… risk… lm no cova… all riskta… 1 0.03 31.2
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.83 0.03 28.6
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.94 0.03 30.0
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.11 0.07 -1.7
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.97 0.03 32.4
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral",
"HS_original_lifeexpectancy", "HS_original_genderequality"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.913 sec elapsed
## Number of specifications: 12
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.62 0.24 -0.26 1.18 0.45 0.73
##
## Descriptive summary of sample sizes:
##
## median min max
## 105487 105487 105487
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.84 0.03 25.4
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.69 0.03 20.9
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.7 0.03 21.8
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.61 0.03 18.9
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.26 0.07 -3.72
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.63 0.03 20.0
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for age-categories
run_specification_for_age <- function(data, age_id, age_label) {
# Daten für die spezifische Altersgruppe filtern
data_subset <- data %>%
filter(age_numeric == age_id)
# Setup für die Spezifikationen durchführen
specification <- setup(
data = data_subset,
y = "risktaking",
x = c("HS_alc_tax_wine", "HS_alc_roaddeath",
"HS_drg_treatment", "HS_nic_affordability", "HS_mh_policy",
"HS_sex_gini", "HS_oth_obesity", "HS_oth_cleancooking",
"HS_mh_mhhospit", "HS_sex_antiretroviral",
"HS_original_lifeexpectancy", "HS_original_genderequality",
"hardship_HS_index"),
model = "lm"
)
# Spezifikationsergebnisse berechnen
specification_results <- specr(specification)
# Statistische Auswertungen drucken mit Alterskategorie-Titel
cat("\nStatistische Ergebnisse für die Alterskategorie:", age_label, "\n")
print(summary(specification_results, digits = 5))
# Grafiken für die spezifische Altersgruppe erzeugen und anzeigen
plot_list <- list(
plot_a = plot(specification_results, type = "curve", ci = FALSE, ribbon = TRUE) +
geom_point(size = 4) + ggtitle(paste("Curve Plot -", age_label)),
plot_b = plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4) + ggtitle(paste("Choices Plot -", age_label)),
plot_c = plot(specification_results, type = "samplesizes") + ylim(0, 400) +
ggtitle(paste("Sample Sizes Plot -", age_label)),
plot_d = plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) + scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "") + ggtitle(paste("Boxplot -", age_label))
)
# Rückgabe der Ergebnisse und Plots
return(list(summary = summary(specification_results, digits = 5), plots = plot_list))
}
# Funktion für jede Altersgruppe aufrufen und sowohl statistische Zusammenfassungen als auch Plots ausgeben
for (i in 1:8) {
results <- run_specification_for_age(hardship_combined, i, paste("Age Group", i))
print(results$summary) # Drucke die Zusammenfassung der Ergebnisse
print(results$plots$plot_a)
print(results$plots$plot_b)
print(results$plots$plot_c)
print(results$plots$plot_d)
}
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 1
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.335 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## -0.22578 0.32286 -1.21618 0.38006 -0.5408 -0.06885
##
## Descriptive summary of sample sizes:
##
## median min max
## 40012 40012 40012
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.380 0.0533 7.13
## 2 HS_alc_road… risk… lm no cova… all riskta… -0.0688 0.0474 -1.45
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.137 0.0492 2.79
## 4 HS_nic_affo… risk… lm no cova… all riskta… -0.322 0.0497 -6.47
## 5 HS_mh_policy risk… lm no cova… all riskta… -1.22 0.112 -10.9
## 6 HS_sex_gini risk… lm no cova… all riskta… -0.541 0.0512 -10.6
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.335 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## -0.22578 0.32286 -1.21618 0.38006 -0.5408 -0.06885
##
## Descriptive summary of sample sizes:
##
## median min max
## 40012 40012 40012
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.380 0.0533 7.13
## 2 HS_alc_road… risk… lm no cova… all riskta… -0.0688 0.0474 -1.45
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.137 0.0492 2.79
## 4 HS_nic_affo… risk… lm no cova… all riskta… -0.322 0.0497 -6.47
## 5 HS_mh_policy risk… lm no cova… all riskta… -1.22 0.112 -10.9
## 6 HS_sex_gini risk… lm no cova… all riskta… -0.541 0.0512 -10.6
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 2
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.407 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.42217 0.50127 -0.62666 1.2058 0.01638 0.59582
##
## Descriptive summary of sample sizes:
##
## median min max
## 50053 50053 50053
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.394 0.0479 8.22
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.596 0.0440 13.6
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.460 0.0452 10.2
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.422 0.0440 9.58
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.627 0.101 -6.20
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.0164 0.0457 0.358
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.407 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.42217 0.50127 -0.62666 1.2058 0.01638 0.59582
##
## Descriptive summary of sample sizes:
##
## median min max
## 50053 50053 50053
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.394 0.0479 8.22
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.596 0.0440 13.6
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.460 0.0452 10.2
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.422 0.0440 9.58
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.627 0.101 -6.20
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.0164 0.0457 0.358
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 3
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.367 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.61579 0.27078 -0.16077 1.75112 0.31477 0.78209
##
## Descriptive summary of sample sizes:
##
## median min max
## 43739 43739 43739
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.709 0.0509 13.9
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.782 0.0516 15.2
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.623 0.0483 12.9
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.616 0.0494 12.5
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.161 0.108 -1.49
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.447 0.0492 9.09
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.367 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.61579 0.27078 -0.16077 1.75112 0.31477 0.78209
##
## Descriptive summary of sample sizes:
##
## median min max
## 43739 43739 43739
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.709 0.0509 13.9
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.782 0.0516 15.2
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.623 0.0483 12.9
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.616 0.0494 12.5
## 5 HS_mh_policy risk… lm no cova… all riskta… -0.161 0.108 -1.49
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.447 0.0492 9.09
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 4
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.319 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.60445 0.38108 0.0553 1.74192 0.33074 0.686
##
## Descriptive summary of sample sizes:
##
## median min max
## 36643 36643 36643
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.686 0.0562 12.2
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.667 0.0608 11.0
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.604 0.0527 11.5
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.611 0.0578 10.6
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.121 0.119 1.02
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.493 0.0544 9.07
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.319 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.60445 0.38108 0.0553 1.74192 0.33074 0.686
##
## Descriptive summary of sample sizes:
##
## median min max
## 36643 36643 36643
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.686 0.0562 12.2
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.667 0.0608 11.0
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.604 0.0527 11.5
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.611 0.0578 10.6
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.121 0.119 1.02
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.493 0.0544 9.07
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 5
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.242 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.78225 0.35601 0.13038 2.55015 0.69605 1.03637
##
## Descriptive summary of sample sizes:
##
## median min max
## 28345 28345 28345
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.782 0.0641 12.2
## 2 HS_alc_road… risk… lm no cova… all riskta… 1.04 0.0750 13.8
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.699 0.0597 11.7
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.854 0.0697 12.3
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.278 0.136 2.05
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.742 0.0632 11.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.242 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.78225 0.35601 0.13038 2.55015 0.69605 1.03637
##
## Descriptive summary of sample sizes:
##
## median min max
## 28345 28345 28345
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.782 0.0641 12.2
## 2 HS_alc_road… risk… lm no cova… all riskta… 1.04 0.0750 13.8
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.699 0.0597 11.7
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.854 0.0697 12.3
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.278 0.136 2.05
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.742 0.0632 11.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 6
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.175 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.89632 0.2858 0.28344 2.82099 0.70355 1.02024
##
## Descriptive summary of sample sizes:
##
## median min max
## 17485 17485 17485
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.912 0.0777 11.7
## 2 HS_alc_road… risk… lm no cova… all riskta… 1.02 0.0982 10.4
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.849 0.0765 11.1
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.896 0.0932 9.62
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.292 0.171 1.70
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.980 0.0825 11.9
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.175 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.89632 0.2858 0.28344 2.82099 0.70355 1.02024
##
## Descriptive summary of sample sizes:
##
## median min max
## 17485 17485 17485
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.912 0.0777 11.7
## 2 HS_alc_road… risk… lm no cova… all riskta… 1.02 0.0982 10.4
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.849 0.0765 11.1
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.896 0.0932 9.62
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.292 0.171 1.70
## 6 HS_sex_gini risk… lm no cova… all riskta… 0.980 0.0825 11.9
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 7
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.1 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.95701 0.29296 0.00237 3.61082 0.9011 1.42481
##
## Descriptive summary of sample sizes:
##
## median min max
## 7198 7198 7198
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.957 0.120 7.96
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.901 0.162 5.55
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.934 0.117 7.96
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.952 0.155 6.13
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.00237 0.259 0.00915
## 6 HS_sex_gini risk… lm no cova… all riskta… 1.42 0.136 10.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.1 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.95701 0.29296 0.00237 3.61082 0.9011 1.42481
##
## Descriptive summary of sample sizes:
##
## median min max
## 7198 7198 7198
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.957 0.120 7.96
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.901 0.162 5.55
## 3 HS_drg_trea… risk… lm no cova… all riskta… 0.934 0.117 7.96
## 4 HS_nic_affo… risk… lm no cova… all riskta… 0.952 0.155 6.13
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.00237 0.259 0.00915
## 6 HS_sex_gini risk… lm no cova… all riskta… 1.42 0.136 10.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 8
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.055 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.34941 0.45962 0.5492 3.96158 1.21032 1.65942
##
## Descriptive summary of sample sizes:
##
## median min max
## 1108 1108 1108
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.549 0.304 1.81
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.586 0.438 1.34
## 3 HS_drg_trea… risk… lm no cova… all riskta… 1.21 0.332 3.65
## 4 HS_nic_affo… risk… lm no cova… all riskta… 1.40 0.394 3.56
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.879 0.677 1.30
## 6 HS_sex_gini risk… lm no cova… all riskta… 1.51 0.333 4.51
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.055 sec elapsed
## Number of specifications: 13
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.34941 0.45962 0.5492 3.96158 1.21032 1.65942
##
## Descriptive summary of sample sizes:
##
## median min max
## 1108 1108 1108
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 HS_alc_tax_… risk… lm no cova… all riskta… 0.549 0.304 1.81
## 2 HS_alc_road… risk… lm no cova… all riskta… 0.586 0.438 1.34
## 3 HS_drg_trea… risk… lm no cova… all riskta… 1.21 0.332 3.65
## 4 HS_nic_affo… risk… lm no cova… all riskta… 1.40 0.394 3.56
## 5 HS_mh_policy risk… lm no cova… all riskta… 0.879 0.677 1.30
## 6 HS_sex_gini risk… lm no cova… all riskta… 1.51 0.333 4.51
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
Hardship Finance
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 8
##
## Specifications:
##
## Independent variable: f_inv_acctownership_primaryedu, f_oth_insfinsvcs_int, f_hs_oopexp10, f_eco_gdpdefl_linked, f_eco_cpi, f_original_gdp, f_original_gini, hardship_Finance_index
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x1203100e0>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 f_inv_acctownership_primaryedu risktaking lm no covariates all risktak…
## 2 f_oth_insfinsvcs_int risktaking lm no covariates all risktak…
## 3 f_hs_oopexp10 risktaking lm no covariates all risktak…
## 4 f_eco_gdpdefl_linked risktaking lm no covariates all risktak…
## 5 f_eco_cpi risktaking lm no covariates all risktak…
## 6 f_original_gdp risktaking lm no covariates all risktak…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 8 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.88 0.37 0.22 2.43 0.66 1.12
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.38 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.87506 0.36897 0.22392 2.4322 0.66037 1.11566
##
## Descriptive summary of sample sizes:
##
## median min max
## 225551 221536 225551
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.224 0.0218 10.3
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.506 0.0202 25.1
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.785 0.0207 38.0
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.712 0.0213 33.4
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.965 0.02 48.3
## 6 f_original_… risk… lm no cova… all riskta… 1.14 0.0233 49.0
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.875 0.369 0.224 2.43 0.660 1.12 225551
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 8 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 f_eco_cpi 0.965 0.965 0.965 0.965 225551
## 2 f_eco_gdpdefl_linked 0.712 0.712 0.712 0.712 221536
## 3 f_hs_oopexp10 0.785 0.785 0.785 0.785 225551
## 4 f_inv_acctownership_primaryedu 0.224 0.224 0.224 0.224 225551
## 5 f_original_gdp 1.14 1.14 1.14 1.14 225551
## 6 f_original_gini 1.11 1.11 1.11 1.11 225551
## 7 f_oth_insfinsvcs_int 0.506 0.506 0.506 0.506 225551
## 8 hardship_Finance_index 2.43 2.43 2.43 2.43 225551
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.738 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.97 0.4 0.27 2.73 0.75 1.21
##
## Descriptive summary of sample sizes:
##
## median min max
## 119591 117485 119591
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.27 0.03 9.14
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.61 0.03 21.8
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.89 0.03 31.6
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.8 0.03 27.7
## 5 f_eco_cpi risk… lm no cova… all riskta… 1.05 0.03 38.7
## 6 f_original_… risk… lm no cova… all riskta… 1.3 0.03 40.6
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.666 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.75 0.28 0.17 2.04 0.55 0.92
##
## Descriptive summary of sample sizes:
##
## median min max
## 105960 104051 105960
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.17 0.03 5.34
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.38 0.03 13.4
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.64 0.03 21.6
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.61 0.03 19.4
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.86 0.03 29.5
## 6 f_original_… risk… lm no cova… all riskta… 0.9 0.03 27.1
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for age-categories
run_specification_for_age <- function(data, age_id, age_label) {
# Daten für die spezifische Altersgruppe filtern
data_subset <- data %>%
filter(age_numeric == age_id)
# Setup für die Spezifikationen durchführen
specification <- setup(
data = data_subset,
y = "risktaking",
x = c("f_inv_acctownership_primaryedu", "f_oth_insfinsvcs_int",
"f_hs_oopexp10", "f_eco_gdpdefl_linked", "f_eco_cpi",
"f_original_gdp", "f_original_gini", "hardship_Finance_index"),
model = "lm"
)
# Spezifikationsergebnisse berechnen
specification_results <- specr(specification)
# Statistische Auswertungen drucken mit Alterskategorie-Titel
cat("\nStatistische Ergebnisse für die Alterskategorie:", age_label, "\n")
print(summary(specification_results, digits = 5))
# Grafiken für die spezifische Altersgruppe erzeugen und anzeigen
plot_list <- list(
plot_a = plot(specification_results, type = "curve", ci = FALSE, ribbon = TRUE) +
geom_point(size = 4) + ggtitle(paste("Curve Plot -", age_label)),
plot_b = plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4) + ggtitle(paste("Choices Plot -", age_label)),
plot_c = plot(specification_results, type = "samplesizes") + ylim(0, 400) +
ggtitle(paste("Sample Sizes Plot -", age_label)),
plot_d = plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) + scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "") + ggtitle(paste("Boxplot -", age_label))
)
# Rückgabe der Ergebnisse und Plots
return(list(summary = summary(specification_results, digits = 5), plots = plot_list))
}
# Funktion für jede Altersgruppe aufrufen und sowohl statistische Zusammenfassungen als auch Plots ausgeben
for (i in 1:8) {
results <- run_specification_for_age(hardship_combined, i, paste("Age Group", i))
print(results$summary) # Drucke die Zusammenfassung der Ergebnisse
print(results$plots$plot_a)
print(results$plots$plot_b)
print(results$plots$plot_c)
print(results$plots$plot_d)
}
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 1
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.198 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.13241 0.23795 -0.88364 0.68027 -0.08151 0.23177
##
## Descriptive summary of sample sizes:
##
## median min max
## 40248 39992 40248
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… -0.884 0.0516 -17.1
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.191 0.0427 4.48
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.680 0.0464 14.6
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.0181 0.0533 0.340
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.0734 0.0525 1.40
## 6 f_original_… risk… lm no cova… all riskta… -0.380 0.052 -7.32
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.198 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.13241 0.23795 -0.88364 0.68027 -0.08151 0.23177
##
## Descriptive summary of sample sizes:
##
## median min max
## 40248 39992 40248
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… -0.884 0.0516 -17.1
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.191 0.0427 4.48
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.680 0.0464 14.6
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.0181 0.0533 0.340
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.0734 0.0525 1.40
## 6 f_original_… risk… lm no cova… all riskta… -0.380 0.052 -7.32
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 2
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.239 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.69668 0.21201 -0.48984 1.678 0.47751 0.75062
##
## Descriptive summary of sample sizes:
##
## median min max
## 50291 49763 50291
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… -0.490 0.0458 -10.7
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.737 0.0390 18.9
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.734 0.0422 17.4
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.390 0.0470 8.29
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.659 0.0451 14.6
## 6 f_original_… risk… lm no cova… all riskta… 0.507 0.0468 10.8
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.239 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.69668 0.21201 -0.48984 1.678 0.47751 0.75062
##
## Descriptive summary of sample sizes:
##
## median min max
## 50291 49763 50291
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… -0.490 0.0458 -10.7
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.737 0.0390 18.9
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.734 0.0422 17.4
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.390 0.0470 8.29
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.659 0.0451 14.6
## 6 f_original_… risk… lm no cova… all riskta… 0.507 0.0468 10.8
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 3
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.212 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.75034 0.37908 -0.15012 1.87 0.39867 0.88125
##
## Descriptive summary of sample sizes:
##
## median min max
## 43981 43230 43981
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… -0.150 0.0477 -3.15
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.404 0.0453 8.93
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.870 0.0457 19.0
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.382 0.0483 7.91
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.756 0.0453 16.7
## 6 f_original_… risk… lm no cova… all riskta… 0.745 0.0529 14.1
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.212 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.75034 0.37908 -0.15012 1.87 0.39867 0.88125
##
## Descriptive summary of sample sizes:
##
## median min max
## 43981 43230 43981
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… -0.150 0.0477 -3.15
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.404 0.0453 8.93
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.870 0.0457 19.0
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.382 0.0483 7.91
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.756 0.0453 16.7
## 6 f_original_… risk… lm no cova… all riskta… 0.745 0.0529 14.1
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 4
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.184 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.65632 0.41376 -0.08145 1.56669 0.25407 0.81955
##
## Descriptive summary of sample sizes:
##
## median min max
## 36781 36047 36781
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… -0.0814 0.0520 -1.57
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.186 0.0515 3.60
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.835 0.0505 16.5
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.277 0.0510 5.43
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.637 0.0480 13.3
## 6 f_original_… risk… lm no cova… all riskta… 0.676 0.0611 11.1
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.184 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.65632 0.41376 -0.08145 1.56669 0.25407 0.81955
##
## Descriptive summary of sample sizes:
##
## median min max
## 36781 36047 36781
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… -0.0814 0.0520 -1.57
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.186 0.0515 3.60
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.835 0.0505 16.5
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.277 0.0510 5.43
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.637 0.0480 13.3
## 6 f_original_… risk… lm no cova… all riskta… 0.676 0.0611 11.1
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 5
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.145 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.65913 0.50266 0.25637 1.85024 0.31763 0.98734
##
## Descriptive summary of sample sizes:
##
## median min max
## 28417 27546 28417
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.274 0.0602 4.55
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.332 0.0610 5.45
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.724 0.0579 12.5
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.256 0.0564 4.54
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.594 0.0508 11.7
## 6 f_original_… risk… lm no cova… all riskta… 0.980 0.0717 13.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.145 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.65913 0.50266 0.25637 1.85024 0.31763 0.98734
##
## Descriptive summary of sample sizes:
##
## median min max
## 28417 27546 28417
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.274 0.0602 4.55
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.332 0.0610 5.45
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.724 0.0579 12.5
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.256 0.0564 4.54
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.594 0.0508 11.7
## 6 f_original_… risk… lm no cova… all riskta… 0.980 0.0717 13.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 6
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.105 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.45447 0.49671 0.08133 1.57527 0.28547 1.05702
##
## Descriptive summary of sample sizes:
##
## median min max
## 17519 16909 17519
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.328 0.0806 4.07
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.0813 0.0777 1.05
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.547 0.0723 7.57
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.158 0.0694 2.27
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.361 0.0621 5.82
## 6 f_original_… risk… lm no cova… all riskta… 0.999 0.0944 10.6
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.105 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.45447 0.49671 0.08133 1.57527 0.28547 1.05702
##
## Descriptive summary of sample sizes:
##
## median min max
## 17519 16909 17519
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.328 0.0806 4.07
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.0813 0.0777 1.05
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.547 0.0723 7.57
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.158 0.0694 2.27
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.361 0.0621 5.82
## 6 f_original_… risk… lm no cova… all riskta… 0.999 0.0944 10.6
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 7
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.057 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.57642 0.65662 -0.18088 1.69236 0.36006 1.24643
##
## Descriptive summary of sample sizes:
##
## median min max
## 7204 6951 7204
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.542 0.130 4.16
## 2 f_oth_insfi… risk… lm no cova… all riskta… -0.181 0.124 -1.46
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.611 0.115 5.32
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.352 0.107 3.28
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.363 0.0963 3.76
## 6 f_original_… risk… lm no cova… all riskta… 1.27 0.154 8.25
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.057 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.57642 0.65662 -0.18088 1.69236 0.36006 1.24643
##
## Descriptive summary of sample sizes:
##
## median min max
## 7204 6951 7204
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.542 0.130 4.16
## 2 f_oth_insfi… risk… lm no cova… all riskta… -0.181 0.124 -1.46
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.611 0.115 5.32
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.352 0.107 3.28
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.363 0.0963 3.76
## 6 f_original_… risk… lm no cova… all riskta… 1.27 0.154 8.25
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 8
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.031 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.66806 0.44276 0.337 1.84195 0.38037 0.99122
##
## Descriptive summary of sample sizes:
##
## median min max
## 1110 1098 1110
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.741 0.333 2.23
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.337 0.277 1.22
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.595 0.299 1.99
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.391 0.283 1.38
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.348 0.297 1.17
## 6 f_original_… risk… lm no cova… all riskta… 1.69 0.376 4.50
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.031 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.66806 0.44276 0.337 1.84195 0.38037 0.99122
##
## Descriptive summary of sample sizes:
##
## median min max
## 1110 1098 1110
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 f_inv_accto… risk… lm no cova… all riskta… 0.741 0.333 2.23
## 2 f_oth_insfi… risk… lm no cova… all riskta… 0.337 0.277 1.22
## 3 f_hs_oopexp… risk… lm no cova… all riskta… 0.595 0.299 1.99
## 4 f_eco_gdpde… risk… lm no cova… all riskta… 0.391 0.283 1.38
## 5 f_eco_cpi risk… lm no cova… all riskta… 0.348 0.297 1.17
## 6 f_original_… risk… lm no cova… all riskta… 1.69 0.376 4.50
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
Hardship Crime
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_theft_estcorruption",
"c_oth_polstab", "hardship_Crime_index"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 8
##
## Specifications:
##
## Independent variable: c_bh_homicide, c_bh_childmalt, c_bh_violextchildprot, c_bh_parviolenceprog, c_bh_elderabuse, c_theft_estcorruption, c_oth_polstab, hardship_Crime_index
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x1265c6bf8>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 c_bh_homicide risktaking lm no covariates all risktaking ~ c_b…
## 2 c_bh_childmalt risktaking lm no covariates all risktaking ~ c_b…
## 3 c_bh_violextchildprot risktaking lm no covariates all risktaking ~ c_b…
## 4 c_bh_parviolenceprog risktaking lm no covariates all risktaking ~ c_b…
## 5 c_bh_elderabuse risktaking lm no covariates all risktaking ~ c_b…
## 6 c_theft_estcorruption risktaking lm no covariates all risktaking ~ c_t…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 8 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.94 0.13 0.43 1.73 0.84 1.02
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.337 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.94298 0.13212 0.43309 1.73386 0.84391 1.0215
##
## Descriptive summary of sample sizes:
##
## median min max
## 225551 225551 225551
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.947 0.0204 46.5
## 2 c_bh_childm… risk… lm no cova… all riskta… 1.01 0.0221 45.7
## 3 c_bh_violex… risk… lm no cova… all riskta… 1.05 0.0227 46.5
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.433 0.0222 19.5
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.939 0.0224 41.9
## 6 c_theft_est… risk… lm no cova… all riskta… 0.685 0.0213 32.2
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.943 0.132 0.433 1.73 0.844 1.02 225551
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 8 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 c_bh_childmalt 1.01 1.01 1.01 1.01 225551
## 2 c_bh_elderabuse 0.939 0.939 0.939 0.939 225551
## 3 c_bh_homicide 0.947 0.947 0.947 0.947 225551
## 4 c_bh_parviolenceprog 0.433 0.433 0.433 0.433 225551
## 5 c_bh_violextchildprot 1.05 1.05 1.05 1.05 225551
## 6 c_oth_polstab 0.897 0.897 0.897 0.897 225551
## 7 c_theft_estcorruption 0.685 0.685 0.685 0.685 225551
## 8 hardship_Crime_index 1.73 1.73 1.73 1.73 225551
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_theft_estcorruption",
"c_oth_polstab", "hardship_Crime_index"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.637 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.01 0.12 0.4 1.87 0.92 1.06
##
## Descriptive summary of sample sizes:
##
## median min max
## 119591 119591 119591
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 1.03 0.03 37.4
## 2 c_bh_childm… risk… lm no cova… all riskta… 1.04 0.03 34.4
## 3 c_bh_violex… risk… lm no cova… all riskta… 1.14 0.03 36.6
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.4 0.03 13.4
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.98 0.03 32.2
## 6 c_theft_est… risk… lm no cova… all riskta… 0.75 0.03 26.0
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_theft_estcorruption",
"c_oth_polstab", "hardship_Crime_index"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.686 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.87 0.16 0.42 1.54 0.69 0.93
##
## Descriptive summary of sample sizes:
##
## median min max
## 105960 105960 105960
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.85 0.03 28.5
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.93 0.03 29.1
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.92 0.03 28.4
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.42 0.03 13.0
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.89 0.03 27.2
## 6 c_theft_est… risk… lm no cova… all riskta… 0.59 0.03 19.0
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for age-categories
run_specification_for_age <- function(data, age_id, age_label) {
# Daten für die spezifische Altersgruppe filtern
data_subset <- data %>%
filter(age_numeric == age_id)
# Setup für die Spezifikationen durchführen
specification <- setup(
data = data_subset,
y = "risktaking",
x = c("c_bh_homicide", "c_bh_childmalt", "c_bh_violextchildprot",
"c_bh_parviolenceprog", "c_bh_elderabuse", "c_theft_estcorruption",
"c_oth_polstab", "hardship_Crime_index"),
model = "lm"
)
# Spezifikationsergebnisse berechnen
specification_results <- specr(specification)
# Statistische Auswertungen drucken mit Alterskategorie-Titel
cat("\nStatistische Ergebnisse für die Alterskategorie:", age_label, "\n")
print(summary(specification_results, digits = 5))
# Grafiken für die spezifische Altersgruppe erzeugen und anzeigen
plot_list <- list(
plot_a = plot(specification_results, type = "curve", ci = FALSE, ribbon = TRUE) +
geom_point(size = 4) + ggtitle(paste("Curve Plot -", age_label)),
plot_b = plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4) + ggtitle(paste("Choices Plot -", age_label)),
plot_c = plot(specification_results, type = "samplesizes") + ylim(0, 400) +
ggtitle(paste("Sample Sizes Plot -", age_label)),
plot_d = plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) + scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "") + ggtitle(paste("Boxplot -", age_label))
)
# Rückgabe der Ergebnisse und Plots
return(list(summary = summary(specification_results, digits = 5), plots = plot_list))
}
# Funktion für jede Altersgruppe aufrufen und sowohl statistische Zusammenfassungen als auch Plots ausgeben
for (i in 1:8) {
results <- run_specification_for_age(hardship_combined, i, paste("Age Group", i))
print(results$summary) # Drucke die Zusammenfassung der Ergebnisse
print(results$plots$plot_a)
print(results$plots$plot_b)
print(results$plots$plot_c)
print(results$plots$plot_d)
}
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 1
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.252 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## -0.07542 0.27515 -0.59613 0.47786 -0.22179 0.11278
##
## Descriptive summary of sample sizes:
##
## median min max
## 40248 40248 40248
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… -0.0942 0.0500 -1.89
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.478 0.0512 9.34
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.381 0.0499 7.64
## 4 c_bh_parvio… risk… lm no cova… all riskta… -0.0566 0.0523 -1.08
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.0235 0.0516 0.455
## 6 c_theft_est… risk… lm no cova… all riskta… -0.596 0.0559 -10.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.252 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## -0.07542 0.27515 -0.59613 0.47786 -0.22179 0.11278
##
## Descriptive summary of sample sizes:
##
## median min max
## 40248 40248 40248
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… -0.0942 0.0500 -1.89
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.478 0.0512 9.34
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.381 0.0499 7.64
## 4 c_bh_parvio… risk… lm no cova… all riskta… -0.0566 0.0523 -1.08
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.0235 0.0516 0.455
## 6 c_theft_est… risk… lm no cova… all riskta… -0.596 0.0559 -10.7
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 2
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.273 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.5746 0.50789 0.14662 1.05317 0.20988 0.88813
##
## Descriptive summary of sample sizes:
##
## median min max
## 50291 50291 50291
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.582 0.045 12.9
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.907 0.0461 19.7
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.882 0.0440 20.1
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.175 0.0468 3.74
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.567 0.0466 12.2
## 6 c_theft_est… risk… lm no cova… all riskta… 0.147 0.0497 2.95
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.273 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.5746 0.50789 0.14662 1.05317 0.20988 0.88813
##
## Descriptive summary of sample sizes:
##
## median min max
## 50291 50291 50291
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.582 0.045 12.9
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.907 0.0461 19.7
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.882 0.0440 20.1
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.175 0.0468 3.74
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.567 0.0466 12.2
## 6 c_theft_est… risk… lm no cova… all riskta… 0.147 0.0497 2.95
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 3
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.222 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.71825 0.26645 0.24177 1.31733 0.47367 0.82103
##
## Descriptive summary of sample sizes:
##
## median min max
## 43981 43981 43981
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.803 0.0455 17.6
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.747 0.0498 15.0
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.875 0.0492 17.8
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.242 0.0492 4.91
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.689 0.0499 13.8
## 6 c_theft_est… risk… lm no cova… all riskta… 0.349 0.0487 7.16
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.222 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.71825 0.26645 0.24177 1.31733 0.47367 0.82103
##
## Descriptive summary of sample sizes:
##
## median min max
## 43981 43981 43981
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.803 0.0455 17.6
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.747 0.0498 15.0
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.875 0.0492 17.8
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.242 0.0492 4.91
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.689 0.0499 13.8
## 6 c_theft_est… risk… lm no cova… all riskta… 0.349 0.0487 7.16
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 4
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.215 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.58486 0.1632 0.22566 1.05406 0.49123 0.68449
##
## Descriptive summary of sample sizes:
##
## median min max
## 36781 36781 36781
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.618 0.0489 12.6
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.552 0.0540 10.2
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.674 0.057 11.8
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.312 0.0531 5.88
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.716 0.0549 13.0
## 6 c_theft_est… risk… lm no cova… all riskta… 0.226 0.0500 4.51
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.215 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.58486 0.1632 0.22566 1.05406 0.49123 0.68449
##
## Descriptive summary of sample sizes:
##
## median min max
## 36781 36781 36781
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.618 0.0489 12.6
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.552 0.0540 10.2
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.674 0.057 11.8
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.312 0.0531 5.88
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.716 0.0549 13.0
## 6 c_theft_est… risk… lm no cova… all riskta… 0.226 0.0500 4.51
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 5
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.149 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.77117 0.36329 0.3742 1.36111 0.53636 0.8775
##
## Descriptive summary of sample sizes:
##
## median min max
## 28417 28417 28417
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.816 0.0536 15.2
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.572 0.0614 9.30
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.789 0.0691 11.4
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.431 0.06 7.18
## 5 c_bh_eldera… risk… lm no cova… all riskta… 1.06 0.0624 17.0
## 6 c_theft_est… risk… lm no cova… all riskta… 0.374 0.0538 6.96
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.149 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.77117 0.36329 0.3742 1.36111 0.53636 0.8775
##
## Descriptive summary of sample sizes:
##
## median min max
## 28417 28417 28417
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.816 0.0536 15.2
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.572 0.0614 9.30
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.789 0.0691 11.4
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.431 0.06 7.18
## 5 c_bh_eldera… risk… lm no cova… all riskta… 1.06 0.0624 17.0
## 6 c_theft_est… risk… lm no cova… all riskta… 0.374 0.0538 6.96
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 6
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.095 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.74127 0.32027 0.29083 1.16023 0.45686 0.86639
##
## Descriptive summary of sample sizes:
##
## median min max
## 17519 17519 17519
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.681 0.0668 10.2
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.467 0.0778 6.00
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.899 0.0918 9.79
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.426 0.0741 5.75
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.802 0.0800 10.0
## 6 c_theft_est… risk… lm no cova… all riskta… 0.291 0.0666 4.37
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.095 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.74127 0.32027 0.29083 1.16023 0.45686 0.86639
##
## Descriptive summary of sample sizes:
##
## median min max
## 17519 17519 17519
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.681 0.0668 10.2
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.467 0.0778 6.00
## 3 c_bh_violex… risk… lm no cova… all riskta… 0.899 0.0918 9.79
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.426 0.0741 5.75
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.802 0.0800 10.0
## 6 c_theft_est… risk… lm no cova… all riskta… 0.291 0.0666 4.37
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 7
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.062 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.73247 0.40732 0.34292 1.30736 0.5863 1.19524
##
## Descriptive summary of sample sizes:
##
## median min max
## 7204 7204 7204
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.665 0.104 6.37
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.573 0.121 4.72
## 3 c_bh_violex… risk… lm no cova… all riskta… 1.28 0.156 8.18
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.591 0.115 5.16
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.800 0.122 6.57
## 6 c_theft_est… risk… lm no cova… all riskta… 0.343 0.105 3.28
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.062 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.73247 0.40732 0.34292 1.30736 0.5863 1.19524
##
## Descriptive summary of sample sizes:
##
## median min max
## 7204 7204 7204
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.665 0.104 6.37
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.573 0.121 4.72
## 3 c_bh_violex… risk… lm no cova… all riskta… 1.28 0.156 8.18
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.591 0.115 5.16
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.800 0.122 6.57
## 6 c_theft_est… risk… lm no cova… all riskta… 0.343 0.105 3.28
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 8
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.03 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.5767 0.3759 0.30284 1.49943 0.36602 0.97964
##
## Descriptive summary of sample sizes:
##
## median min max
## 1110 1110 1110
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.303 0.272 1.11
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.651 0.313 2.08
## 3 c_bh_violex… risk… lm no cova… all riskta… 1.50 0.417 3.60
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.374 0.304 1.23
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.502 0.340 1.48
## 6 c_theft_est… risk… lm no cova… all riskta… 0.343 0.274 1.25
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.03 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.5767 0.3759 0.30284 1.49943 0.36602 0.97964
##
## Descriptive summary of sample sizes:
##
## median min max
## 1110 1110 1110
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 c_bh_homici… risk… lm no cova… all riskta… 0.303 0.272 1.11
## 2 c_bh_childm… risk… lm no cova… all riskta… 0.651 0.313 2.08
## 3 c_bh_violex… risk… lm no cova… all riskta… 1.50 0.417 3.60
## 4 c_bh_parvio… risk… lm no cova… all riskta… 0.374 0.304 1.23
## 5 c_bh_eldera… risk… lm no cova… all riskta… 0.502 0.340 1.48
## 6 c_theft_est… risk… lm no cova… all riskta… 0.343 0.274 1.25
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
Hardship Environment
Setup for specifications
library(specr)
# Setup für die Spezifikationen mit einer umfassenderen Auswahl von Variablen
specification <- setup(
data = hardship_combined,
y = "risktaking", # abhängige Variable
x = c("e_oth_drinkingwater",
"e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school", "e_exp_disaster",
"e_exp_airdeath100k", "e_exp_watersanithyg", "hardship_environment_index"),
model = "lm"
)
# Zusammenfassung der Spezifikationen
summary(specification)
## Setup for the Specification Curve Analysis
## -------------------------------------------
## Class: specr.setup -- version: 1.0.1
## Number of specifications: 8
##
## Specifications:
##
## Independent variable: e_oth_drinkingwater, e_exp_watersanithyg100k, e_ses_gini, e_ses_school, e_exp_disaster, e_exp_airdeath100k, e_exp_watersanithyg, hardship_environment_index
## Dependent variable: risktaking
## Models: lm
## Covariates: no covariates
## Subsets analyses: all
##
## Function used to extract parameters:
##
## function (x)
## broom::tidy(x, conf.int = TRUE)
## <environment: 0x10b53fe00>
##
##
## Head of specifications table (first 6 rows):
## # A tibble: 6 × 6
## x y model controls subsets formula
## <chr> <chr> <chr> <chr> <chr> <glue>
## 1 e_oth_drinkingwater risktaking lm no covariates all risktaking ~ e…
## 2 e_exp_watersanithyg100k risktaking lm no covariates all risktaking ~ e…
## 3 e_ses_gini risktaking lm no covariates all risktaking ~ e…
## 4 e_ses_school risktaking lm no covariates all risktaking ~ e…
## 5 e_exp_disaster risktaking lm no covariates all risktaking ~ e…
## 6 e_exp_airdeath100k risktaking lm no covariates all risktaking ~ e…
run specifications
specification_results <- specr(specification)
specification_results
## Models fitted based on 8 specifications
## Number of cores used: 1
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.06 0.32 -0.07 2.64 0.84 1.25
summary(specification_results, digits = 5)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 1.512 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.0552 0.31972 -0.06984 2.63689 0.83561 1.24921
##
## Descriptive summary of sample sizes:
##
## median min max
## 224550 224550 224550
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 1.23 0.0253 48.6
## 2 e_exp_water… risk… lm no cova… all riskta… 1.31 0.0222 59.1
## 3 e_ses_gini risk… lm no cova… all riskta… 0.941 0.0206 45.8
## 4 e_ses_school risk… lm no cova… all riskta… 0.634 0.0271 23.4
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.0698 0.021 -3.33
## 6 e_exp_airde… risk… lm no cova… all riskta… 1.17 0.0215 54.4
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
summarizing the parameter distribution
summary(specification_results, type = "curve")
## # A tibble: 1 × 7
## median mad min max q25 q75 obs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.06 0.320 -0.0698 2.64 0.836 1.25 224550
summary(specification_results,
type = "curve",
group = "x",
stats = c("median", "mean", "min", "max")) # Statistiken in einem Vektor auflisten
## # A tibble: 8 × 6
## x median mean min max obs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 e_exp_airdeath100k 1.17 1.17 1.17 1.17 224550
## 2 e_exp_disaster -0.0698 -0.0698 -0.0698 -0.0698 224550
## 3 e_exp_watersanithyg 0.903 0.903 0.903 0.903 224550
## 4 e_exp_watersanithyg100k 1.31 1.31 1.31 1.31 224550
## 5 e_oth_drinkingwater 1.23 1.23 1.23 1.23 224550
## 6 e_ses_gini 0.941 0.941 0.941 0.941 224550
## 7 e_ses_school 0.634 0.634 0.634 0.634 224550
## 8 hardship_environment_index 2.64 2.64 2.64 2.64 224550
Plots
plot(specification_results)
(a <- plot(specification_results, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b <- plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c <- plot(specification_results, type = "samplesizes") + ylim(0, 400))
plot_grid(a, b, c, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for males
specification_males <- setup(
data = hardship_combined %>%
filter(gender == 1), # Filter for males
y = "risktaking",
x = c("e_oth_drinkingwater",
"e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school", "e_exp_disaster",
"e_exp_airdeath100k", "e_exp_watersanithyg", "hardship_environment_index"),
model = "lm"
)
# Run the specifications for males
specification_results_males <- specr(specification_males)
# View the summary of the results
summary(specification_results_males)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.632 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.16 0.41 -0.05 2.96 0.96 1.42
##
## Descriptive summary of sample sizes:
##
## median min max
## 119093 119093 119093
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 1.41 0.04 39.8
## 2 e_exp_water… risk… lm no cova… all riskta… 1.46 0.03 48.1
## 3 e_ses_gini risk… lm no cova… all riskta… 1.04 0.03 37.3
## 4 e_ses_school risk… lm no cova… all riskta… 0.72 0.04 18.8
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.05 0.03 -1.62
## 6 e_exp_airde… risk… lm no cova… all riskta… 1.28 0.03 43.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for male subset results
plot(specification_results_males)
(a_male <- plot(specification_results_males, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_male <- plot(specification_results_males, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_male <- plot(specification_results_males, type = "samplesizes") + ylim(0, 400))
plot_grid(a_male, b_male, c_male, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_males, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for females
specification_females <- setup(
data = hardship_combined %>%
filter(gender == 0), # Filter for females
y = "risktaking",
x = c("e_oth_drinkingwater",
"e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school", "e_exp_disaster",
"e_exp_airdeath100k", "e_exp_watersanithyg", "hardship_environment_index"),
model = "lm"
)
# Run the specifications for females
specification_results_females <- specr(specification_females)
# View the summary of the results
summary(specification_results_females)
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.574 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.88 0.31 -0.14 2.11 0.62 1.02
##
## Descriptive summary of sample sizes:
##
## median min max
## 105457 105457 105457
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 0.96 0.04 27.0
## 2 e_exp_water… risk… lm no cova… all riskta… 1.07 0.03 33.5
## 3 e_ses_gini risk… lm no cova… all riskta… 0.8 0.03 26.5
## 4 e_ses_school risk… lm no cova… all riskta… 0.51 0.04 13.2
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.14 0.03 -4.63
## 6 e_exp_airde… risk… lm no cova… all riskta… 1 0.03 32.2
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
Plots for female subset results
plot(specification_results_females)
(a_female <- plot(specification_results_females, type = "curve", ci = F, ribbon = T) +
geom_point(size = 4))
(b_female <- plot(specification_results_females, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4))
(c_female <- plot(specification_results_females, type = "samplesizes") + ylim(0, 400))
plot_grid(a_female, b_female, c_female, ncol = 1,
align = "v",
rel_heights = c(1.5, 2, 0.8),
axis = "rbl")
plot(specification_results_females, type = "boxplot") +
geom_point(alpha = .4) +
scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "")
Subsetting data for age-categories
run_specification_for_age <- function(data, age_id, age_label) {
# Daten für die spezifische Altersgruppe filtern
data_subset <- data %>%
filter(age_numeric == age_id)
# Setup für die Spezifikationen durchführen
specification <- setup(
data = data_subset,
y = "risktaking",
x = c("e_oth_drinkingwater",
"e_exp_watersanithyg100k", "e_ses_gini", "e_ses_school", "e_exp_disaster",
"e_exp_airdeath100k", "e_exp_watersanithyg", "hardship_environment_index"),
model = "lm"
)
# Spezifikationsergebnisse berechnen
specification_results <- specr(specification)
# Statistische Auswertungen drucken mit Alterskategorie-Titel
cat("\nStatistische Ergebnisse für die Alterskategorie:", age_label, "\n")
print(summary(specification_results, digits = 5))
# Grafiken für die spezifische Altersgruppe erzeugen und anzeigen
plot_list <- list(
plot_a = plot(specification_results, type = "curve", ci = FALSE, ribbon = TRUE) +
geom_point(size = 4) + ggtitle(paste("Curve Plot -", age_label)),
plot_b = plot(specification_results, type = "choices", choices = c("x", "y", "model", "controls")) +
geom_point(size = 2, shape = 4) + ggtitle(paste("Choices Plot -", age_label)),
plot_c = plot(specification_results, type = "samplesizes") + ylim(0, 400) +
ggtitle(paste("Sample Sizes Plot -", age_label)),
plot_d = plot(specification_results, type = "boxplot") +
geom_point(alpha = .4) + scale_fill_brewer(palette = "Pastel2") +
labs(x = "Effect size", fill = "") + ggtitle(paste("Boxplot -", age_label))
)
# Rückgabe der Ergebnisse und Plots
return(list(summary = summary(specification_results, digits = 5), plots = plot_list))
}
# Funktion für jede Altersgruppe aufrufen und sowohl statistische Zusammenfassungen als auch Plots ausgeben
for (i in 1:8) {
results <- run_specification_for_age(hardship_combined, i, paste("Age Group", i))
print(results$summary) # Drucke die Zusammenfassung der Ergebnisse
print(results$plots$plot_a)
print(results$plots$plot_b)
print(results$plots$plot_c)
print(results$plots$plot_d)
}
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 1
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.201 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## -0.11647 0.0701 -0.31557 0.20319 -0.15517 -0.08139
##
## Descriptive summary of sample sizes:
##
## median min max
## 40131 40131 40131
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… -0.147 0.0490 -2.99
## 2 e_exp_water… risk… lm no cova… all riskta… -0.100 0.0498 -2.01
## 3 e_ses_gini risk… lm no cova… all riskta… 0.203 0.0464 4.38
## 4 e_ses_school risk… lm no cova… all riskta… -0.0342 0.0508 -0.673
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.316 0.0528 -5.98
## 6 e_exp_airde… risk… lm no cova… all riskta… -0.181 0.0536 -3.38
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.201 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## -0.11647 0.0701 -0.31557 0.20319 -0.15517 -0.08139
##
## Descriptive summary of sample sizes:
##
## median min max
## 40131 40131 40131
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… -0.147 0.0490 -2.99
## 2 e_exp_water… risk… lm no cova… all riskta… -0.100 0.0498 -2.01
## 3 e_ses_gini risk… lm no cova… all riskta… 0.203 0.0464 4.38
## 4 e_ses_school risk… lm no cova… all riskta… -0.0342 0.0508 -0.673
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.316 0.0528 -5.98
## 6 e_exp_airde… risk… lm no cova… all riskta… -0.181 0.0536 -3.38
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 2
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.244 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.67314 0.22178 -0.14896 1.59072 0.38994 0.76119
##
## Descriptive summary of sample sizes:
##
## median min max
## 50028 50028 50028
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 0.675 0.0442 15.3
## 2 e_exp_water… risk… lm no cova… all riskta… 0.760 0.0449 16.9
## 3 e_ses_gini risk… lm no cova… all riskta… 0.671 0.0426 15.8
## 4 e_ses_school risk… lm no cova… all riskta… 0.164 0.0492 3.33
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.149 0.0477 -3.12
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.764 0.0477 16.0
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.244 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.67314 0.22178 -0.14896 1.59072 0.38994 0.76119
##
## Descriptive summary of sample sizes:
##
## median min max
## 50028 50028 50028
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 0.675 0.0442 15.3
## 2 e_exp_water… risk… lm no cova… all riskta… 0.760 0.0449 16.9
## 3 e_ses_gini risk… lm no cova… all riskta… 0.671 0.0426 15.8
## 4 e_ses_school risk… lm no cova… all riskta… 0.164 0.0492 3.33
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.149 0.0477 -3.12
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.764 0.0477 16.0
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 3
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.218 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.7772 0.25192 -0.26951 1.83427 0.5977 0.92031
##
## Descriptive summary of sample sizes:
##
## median min max
## 43722 43722 43722
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 0.904 0.0590 15.3
## 2 e_exp_water… risk… lm no cova… all riskta… 0.971 0.0508 19.1
## 3 e_ses_gini risk… lm no cova… all riskta… 0.745 0.0458 16.2
## 4 e_ses_school risk… lm no cova… all riskta… 0.498 0.0593 8.41
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.270 0.0480 -5.61
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.810 0.0491 16.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.218 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.7772 0.25192 -0.26951 1.83427 0.5977 0.92031
##
## Descriptive summary of sample sizes:
##
## median min max
## 43722 43722 43722
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 0.904 0.0590 15.3
## 2 e_exp_water… risk… lm no cova… all riskta… 0.971 0.0508 19.1
## 3 e_ses_gini risk… lm no cova… all riskta… 0.745 0.0458 16.2
## 4 e_ses_school risk… lm no cova… all riskta… 0.498 0.0593 8.41
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.270 0.0480 -5.61
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.810 0.0491 16.5
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 4
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.189 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.67373 0.20809 -0.263 1.66122 0.64445 0.91915
##
## Descriptive summary of sample sizes:
##
## median min max
## 36596 36596 36596
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 0.902 0.0775 11.6
## 2 e_exp_water… risk… lm no cova… all riskta… 0.971 0.0578 16.8
## 3 e_ses_gini risk… lm no cova… all riskta… 0.652 0.0506 12.9
## 4 e_ses_school risk… lm no cova… all riskta… 0.621 0.0774 8.03
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.263 0.0488 -5.39
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.678 0.0525 12.9
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.189 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.67373 0.20809 -0.263 1.66122 0.64445 0.91915
##
## Descriptive summary of sample sizes:
##
## median min max
## 36596 36596 36596
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 0.902 0.0775 11.6
## 2 e_exp_water… risk… lm no cova… all riskta… 0.971 0.0578 16.8
## 3 e_ses_gini risk… lm no cova… all riskta… 0.652 0.0506 12.9
## 4 e_ses_school risk… lm no cova… all riskta… 0.621 0.0774 8.03
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.263 0.0488 -5.39
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.678 0.0525 12.9
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 5
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.144 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.82666 0.36223 -0.06869 2.10444 0.74042 1.22555
##
## Descriptive summary of sample sizes:
##
## median min max
## 28300 28300 28300
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 1.53 0.0991 15.4
## 2 e_exp_water… risk… lm no cova… all riskta… 1.12 0.0674 16.7
## 3 e_ses_gini risk… lm no cova… all riskta… 0.856 0.0579 14.8
## 4 e_ses_school risk… lm no cova… all riskta… 0.635 0.102 6.21
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.0687 0.0523 -1.31
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.775 0.0581 13.3
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.144 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 0.82666 0.36223 -0.06869 2.10444 0.74042 1.22555
##
## Descriptive summary of sample sizes:
##
## median min max
## 28300 28300 28300
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 1.53 0.0991 15.4
## 2 e_exp_water… risk… lm no cova… all riskta… 1.12 0.0674 16.7
## 3 e_ses_gini risk… lm no cova… all riskta… 0.856 0.0579 14.8
## 4 e_ses_school risk… lm no cova… all riskta… 0.635 0.102 6.21
## 5 e_exp_disas… risk… lm no cova… all riskta… -0.0687 0.0523 -1.31
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.775 0.0581 13.3
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 6
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.104 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.00315 0.74203 0.06936 2.46486 0.57135 1.47138
##
## Descriptive summary of sample sizes:
##
## median min max
## 17471 17471 17471
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 1.94 0.146 13.3
## 2 e_exp_water… risk… lm no cova… all riskta… 1.31 0.0877 15.0
## 3 e_ses_gini risk… lm no cova… all riskta… 1.05 0.0733 14.4
## 4 e_ses_school risk… lm no cova… all riskta… 0.365 0.158 2.31
## 5 e_exp_disas… risk… lm no cova… all riskta… 0.0694 0.0614 1.13
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.640 0.0738 8.67
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.104 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.00315 0.74203 0.06936 2.46486 0.57135 1.47138
##
## Descriptive summary of sample sizes:
##
## median min max
## 17471 17471 17471
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 1.94 0.146 13.3
## 2 e_exp_water… risk… lm no cova… all riskta… 1.31 0.0877 15.0
## 3 e_ses_gini risk… lm no cova… all riskta… 1.05 0.0733 14.4
## 4 e_ses_school risk… lm no cova… all riskta… 0.365 0.158 2.31
## 5 e_exp_disas… risk… lm no cova… all riskta… 0.0694 0.0614 1.13
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.640 0.0738 8.67
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 7
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.063 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.19034 0.62619 0.15242 2.95996 0.80354 1.67544
##
## Descriptive summary of sample sizes:
##
## median min max
## 7193 7193 7193
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 2.45 0.250 9.78
## 2 e_exp_water… risk… lm no cova… all riskta… 1.41 0.139 10.2
## 3 e_ses_gini risk… lm no cova… all riskta… 0.967 0.115 8.42
## 4 e_ses_school risk… lm no cova… all riskta… 0.697 0.276 2.53
## 5 e_exp_disas… risk… lm no cova… all riskta… 0.152 0.0935 1.63
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.839 0.117 7.17
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.063 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.19034 0.62619 0.15242 2.95996 0.80354 1.67544
##
## Descriptive summary of sample sizes:
##
## median min max
## 7193 7193 7193
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 2.45 0.250 9.78
## 2 e_exp_water… risk… lm no cova… all riskta… 1.41 0.139 10.2
## 3 e_ses_gini risk… lm no cova… all riskta… 0.967 0.115 8.42
## 4 e_ses_school risk… lm no cova… all riskta… 0.697 0.276 2.53
## 5 e_exp_disas… risk… lm no cova… all riskta… 0.152 0.0935 1.63
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.839 0.117 7.17
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
##
## Statistische Ergebnisse für die Alterskategorie: Age Group 8
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.035 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.42662 1.06436 0.16285 3.62138 0.80264 2.14885
##
## Descriptive summary of sample sizes:
##
## median min max
## 1109 1109 1109
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 3.12 0.570 5.47
## 2 e_exp_water… risk… lm no cova… all riskta… 1.77 0.357 4.98
## 3 e_ses_gini risk… lm no cova… all riskta… 0.521 0.299 1.74
## 4 e_ses_school risk… lm no cova… all riskta… 1.08 0.562 1.92
## 5 e_exp_disas… risk… lm no cova… all riskta… 0.163 0.247 0.660
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.897 0.299 3.00
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
## Results of the specification curve analysis
## -------------------
## Technical details:
##
## Class: specr.object -- version: 1.0.1
## Cores used: 1
## Duration of fitting process: 0.035 sec elapsed
## Number of specifications: 8
##
## Descriptive summary of the specification curve:
##
## median mad min max q25 q75
## 1.42662 1.06436 0.16285 3.62138 0.80264 2.14885
##
## Descriptive summary of sample sizes:
##
## median min max
## 1109 1109 1109
##
## Head of the specification results (first 6 rows):
##
## # A tibble: 6 × 24
## x y model controls subsets formula estimate std.error statistic
## <chr> <chr> <chr> <chr> <chr> <glue> <dbl> <dbl> <dbl>
## 1 e_oth_drink… risk… lm no cova… all riskta… 3.12 0.570 5.47
## 2 e_exp_water… risk… lm no cova… all riskta… 1.77 0.357 4.98
## 3 e_ses_gini risk… lm no cova… all riskta… 0.521 0.299 1.74
## 4 e_ses_school risk… lm no cova… all riskta… 1.08 0.562 1.92
## 5 e_exp_disas… risk… lm no cova… all riskta… 0.163 0.247 0.660
## 6 e_exp_airde… risk… lm no cova… all riskta… 0.897 0.299 3.00
## # ℹ 15 more variables: p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
## # fit_r.squared <dbl>, fit_adj.r.squared <dbl>, fit_sigma <dbl>,
## # fit_statistic <dbl>, fit_p.value <dbl>, fit_df <dbl>, fit_logLik <dbl>,
## # fit_AIC <dbl>, fit_BIC <dbl>, fit_deviance <dbl>, fit_df.residual <dbl>,
## # fit_nobs <dbl>
## NULL
